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Ph.D Theses

Proteins are involved in almost all functions in our cells due to their ability to combine
conformational motion with chemical specificity. Hence, information about the motions of a
protein provides insights into its function. Proteins move on a rugged energy landscape with
many local minima, which is imposed on their high-dimensional conformational space. Exhaustive
sampling of this space exceeds the available computational resources for all but the smallest
proteins. Computational approaches thus have to simplify the potential energy function and/or
resolution of the model using information about what is relevant and what can be ignored. The
accuracy of the approximation depends on the accuracy of the used information. Information
that is specific to the problem domain, i.e. protein motion in our case, usually results in better
models.

In this thesis, I propose a novel elastic network model of learned maintained contacts, lmcENM.
It expands the range of motions that can be captured by such simplified models by leveraging
novel information about a protein’s structure. This improves the general applicability of elastic
network models.

Intelligent robots must be able to learn; they must be able to adapt their behavior based on experience. But generalization from past experience is only possible based on assumptions or prior knowledge (priors for short) about how the world works.

I study the role of these priors for learning perception. Although priors play a central role in machine learning, they are often hidden in the details of learning algorithms. By making these priors explicit, we can see that currently used priors describe the world from the perspective of a passive disinterested observer. Such generic AI priors are useful because they apply to perception scenarios where there is no robot, such as image classification. These priors are still useful for learning robotic perception, but they miss an important aspect of the problem: the robot.

In this thesis we study robot perception to support a specific type of manipulation task in unstructured environments, the mechanical manipulation of kinematic degrees of freedom. We propose a general approach for interactive perception and instantiations of this approach into perceptual systems to build kinematic, geometric and dynamic models of articulated objects.

Reinforcement learning is a computational framework that enables machines to learn from trial-and-error interaction with the environment. In recent years, reinforcement learning has been successfully applied to a wide variety of problem domains, including robotics. However, the success of the reinforcement learning applications in robotics relies on a variety of assumptions, such as the availability of large amounts of training data, highly accurate models of the robot and the environment as well as prior knowledge about the task.
In this thesis, we study several of these assumptions and investigate how to generalize them. To that end, we look at these assumptions from different angles. On the one hand, we study them in two concrete applications of reinforcement learning in robotics: ball catching and learning to manipulate articulated objects. On the other hand, we develop an abstract explanatory framework that relates the assumptions to the decomposability of problems and solutions. Taken together, the concrete case studies and the abstract explanatory framework enable us to make suggestions on how to relax the previously stated assumptions and how to design more effective solutions to robot reinforcement learning problems.

Raphael Deimel's thesis reconsiders hand design from the perspective of providing first and foremost robust and reliable grasping, instead of precise control of posture and simple mechanical modelabilty. This results in a fundamentally different manipulator hardware, so called soft hands, that are made out of rubber and fibers which make them highly adaptable. His thesis covers not only hand designs, but also provides an elaborate collection of methods to design, simulate and rapidly prototype soft robots, referred to as the "PneuFlex toolkit".

The key features of this system are a high degree of immersion into the computer generated virtual environment and a large working volume. The high degree of immersion will be achieved by multimodal human-exoskeleton interaction based on haptic effects, audio and three- dimensional visualization. The large working volume will be achieved by a lightweight wearable construction that can be carried on the back of the user.

Computationally efficient motion planning mus avoid exhaustive exploration of high-dimensional configuration spaces by leveraging the structure present in real-world planning problems. We argue that this can be accomplished most effectively by carefully balancing exploration and exploitation.

Exploration seeks to understand configuration space, irrespective of the planning problem, and exploitation acts to solve the problem, given the available information obtained by exploration. We present an exploring/exploiting tree (EET) planner that balances its exploration and exploitation behavior.

The planner acquires workspace information and subsequently uses this information for exploitation in configuration space. If exploitation fails in difficult regions the planner gradually shifts to its behavior towards exploration.

This thesis develops robotic skills for manipulating novel articulated objects. The degrees of freedom of an articulated object describe the relationship among its rigid bodies, and are often relevant to the object's intended function. Examples of everyday articulated objects include scissors, pliers, doors, door handles, books, and drawers. Autonomous manipulation of articulated objects is therefore a prerequisite for many robotic applications in our everyday environments.

The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima.

Robots already impact the way we understand our world and live our lives. However, their impact and use is limited by the skills they possess. Currently deployed autonomous robots lack the manipulation skills possessed by humans. To achieve general autonomy and applicability in the real world, robots must possess such skills.

Diploma / Master Theses

In this thesis, we present an approach for retrieving templates that is inde-
pendent of sequence similarity. In order to do this, we combine ab initio with
comparative modeling by looking for similarities in ab initio decoys to identify good
templates.

In this thesis we present a new contact predictor that combines evolutionary, sequence-based and physicochemical information. The contact predictor uses a new and refined feature set with drastically reduced dimensionality.

Interactive Perception exploits the robot capabilities to interact with the environment to reveal hidden properties, like the kinematic structures of articulated objects. However, when the robot faces a new environment, it needs to decide on how to interact to maximize the information gain based on sensor data, and use compliant controllers that allow the articulation to guide the motion...

The ability to selectively stiffen otherwise compliant soft actuators increases their versatility and dexterity. The thesis investigates granular jamming and layer jamming as two possible methods to achieve stiffening with PneuFlex actuators, a type of soft continuum actuator. It details five designs of jamming compartments that can be attached to an actuator. The strength of the most effective prototype based on layer jamming is also validated in the context of pushing buttons.

We present an incremental method for motion generation in environments with unpredictably moving and initially unknown obstacles. The key to the method is its incremental nature: it locally augments and adapts global motion plans in response to changes in the environment, even if they significantly change the connectivity of the world.

Sequence alignment methods are frequently used in protein structure prediction to identify homologous protein structures. The existing methods make local and global alignments between sequentially contiguous protein sequences. However, in our ongoing protein structure prediction research, we have a unique sequence to sequence alignment problem. The potential sequence alignments need to be made between a target sequence and the sets of sequence fragments, where the sequence fragments may not be sequentially contiguous.

Haptic devices enhance the range of multi-modal interaction in virtual reality environments. With the wearable haptic device, developed at the RBO Lab, this interaction is not limited to a small workspace any longer. The wider range of motion allows for new application scenarios.

Sensing for soft continuum actuators as a necessary technology has emerged recently with the development of so called soft hands, which exploit the high deformability of soft structures and materials. Unfortunately, soft, stretchable sensors capable of withstanding a stretch of 100% are commercially not available. At the same time their tight integration into actuators is required to address the specific challenges of continuously deforming actuators. The thesis evaluates three potential sensor technologies for their suitability in soft hands. The thesis investigates their robustness, ease of use, long term stability and responsiveness with respect to the intended application in soft hands.

To extend the field of application of robots in unstructured environments it is necessary to develop new techniques of environment perception and interpretation. These methods must give machines the capability to generate sufficient information, which enables them to fulfil their tasks with the aid of their sensors. Therefore it is required to extract local and task dependent invariant structure out of the unstructured environment.
more to: Position-Based Servoing via Probabilistic Part-Based Object Models

Predictive state representations (PSRs) are gaining a lot of attention in the robotics community lately because, in theory, they promise a powerful model that might be learned directly from data. But the practical application of PSRs remains a difficult procedure. In this practical guide we aim to ease and encourage practical work with PSRs.
more to: A Practical Guide to Transformed Predictive State Representations